Shulong Li

609 total citations
24 papers, 437 citations indexed

About

Shulong Li is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Biomedical Engineering. According to data from OpenAlex, Shulong Li has authored 24 papers receiving a total of 437 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Radiology, Nuclear Medicine and Imaging, 9 papers in Pulmonary and Respiratory Medicine and 7 papers in Biomedical Engineering. Recurrent topics in Shulong Li's work include Radiomics and Machine Learning in Medical Imaging (15 papers), Advanced X-ray and CT Imaging (5 papers) and Pancreatic and Hepatic Oncology Research (5 papers). Shulong Li is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (15 papers), Advanced X-ray and CT Imaging (5 papers) and Pancreatic and Hepatic Oncology Research (5 papers). Shulong Li collaborates with scholars based in China, United States and Taiwan. Shulong Li's co-authors include Jing Wang, Jen-Chih Yao, Yeong‐Cheng Liou, Chong Li, Michael R. Folkert, Zhiguo Zhou, Liyuan Chen, Hongxia Hao, Chenyang Shen and Steve Jiang and has published in prestigious journals such as Physics in Medicine and Biology, Annals of Surgical Oncology and Industrial Crops and Products.

In The Last Decade

Shulong Li

23 papers receiving 422 citations

Peers

Shulong Li
Yoshiko Yamashita Japan
Xingwei Wang China
Wentian Guo China
Xiangsong Zhang China
Yujia Liu China
J Alaly United States
Yuebin Liang China
Andrei Pugachev United States
Wang An-yu China
Shengtian Sang United States
Yoshiko Yamashita Japan
Shulong Li
Citations per year, relative to Shulong Li Shulong Li (= 1×) peers Yoshiko Yamashita

Countries citing papers authored by Shulong Li

Since Specialization
Citations

This map shows the geographic impact of Shulong Li's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Shulong Li with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shulong Li more than expected).

Fields of papers citing papers by Shulong Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shulong Li. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Shulong Li. The network helps show where Shulong Li may publish in the future.

Co-authorship network of co-authors of Shulong Li

This figure shows the co-authorship network connecting the top 25 collaborators of Shulong Li. A scholar is included among the top collaborators of Shulong Li based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Shulong Li. Shulong Li is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Li, Shulong, et al.. (2025). EGF-Former: An efficient network for structural segmentation and phenotype extraction of sweet peppers in complex environments. Industrial Crops and Products. 227. 120850–120850.
2.
Chen, Hao, Yi Chen, Yanfeng Hu, et al.. (2024). 18F-FDG PET/CT Radiomics-Based Multimodality Fusion Model for Preoperative Individualized Noninvasive Prediction of Peritoneal Metastasis in Advanced Gastric Cancer. Annals of Surgical Oncology. 31(9). 6017–6027. 6 indexed citations
3.
Sun, Miao, et al.. (2024). A study on hyperspectral soil total nitrogen inversion using a hybrid deep learning model CBiResNet-BiLSTM. Chemical and Biological Technologies in Agriculture. 11(1). 6 indexed citations
4.
5.
Zhang, Haojie, et al.. (2024). Multiscale and multiperception feature learning for pancreatic lesion detection based on noncontrast CT. Physics in Medicine and Biology. 69(10). 105014–105014. 3 indexed citations
6.
Jiang, Donghui, et al.. (2024). EFS-Former: An Efficient Network for Fruit Tree Leaf Disease Segmentation and Severity Assessment. Agronomy. 14(9). 1992–1992. 1 indexed citations
7.
Hu, Xingbin, Shulong Li, Zhiqi Yao, et al.. (2023). Glutamine metabolic microenvironment drives M2 macrophage polarization to mediate trastuzumab resistance in HER2‐positive gastric cancer. Cancer Communications. 43(8). 909–937. 61 indexed citations
8.
Zhang, Haojie, Qifan Zhang, Xuemin Chen, et al.. (2022). A multimodal model fusing multiphase contrast-enhanced CT and clinical characteristics for predicting lymph node metastases of pancreatic cancer. Physics in Medicine and Biology. 67(17). 175002–175002. 7 indexed citations
9.
Shen, Chenyang, Liyuan Chen, Shulong Li, et al.. (2020). On the robustness of deep learning-based lung-nodule classification for CT images with respect to image noise. Physics in Medicine and Biology. 65(24). 245037–245037. 17 indexed citations
10.
Li, Shulong, Bin Li, Liyuan Chen, et al.. (2019). Predicting lung nodule malignancies by combining deep convolutional neural network and handcrafted features. Physics in Medicine and Biology. 64(17). 175012–175012. 61 indexed citations
11.
Chen, Liyuan, Jing Wang, Shulong Li, et al.. (2019). A manifold learning regularization approach to enhance 3D CT image-based lung nodule classification. International Journal of Computer Assisted Radiology and Surgery. 15(2). 287–295. 50 indexed citations
12.
Zhang, Hang, Qing Li, Shulong Li, Jianhua Ma, & Jing Huang. (2019). [A radiomic approach to differential diagnosis of renal cell carcinoma in patients with hydronephrosis and renal calculi].. PubMed. 39(5). 547–553. 4 indexed citations
13.
Zhou, Zhiguo, et al.. (2019). A collection input based support tensor machine for lesion malignancy classification in digital breast tomosynthesis. Physics in Medicine and Biology. 64(23). 235007–235007. 6 indexed citations
14.
Zhou, Zhiguo, Shulong Li, Genggeng Qin, et al.. (2019). Multi-Objective-Based Radiomic Feature Selection for Lesion Malignancy Classification. IEEE Journal of Biomedical and Health Informatics. 24(1). 194–204. 27 indexed citations
15.
Hao, Hongxia, Zhiguo Zhou, Shulong Li, et al.. (2018). Shell feature: a new radiomics descriptor for predicting distant failure after radiotherapy in non-small cell lung cancer and cervix cancer. Physics in Medicine and Biology. 63(9). 95007–95007. 47 indexed citations
16.
Zhou, Zhiguo, Shulong Li, Genggeng Qin, et al.. (2018). Multi-objective based radiomic feature selection for lesion malignancy classification. 3 indexed citations
17.
Li, Shulong, Ning Yang, Bin Li, et al.. (2018). A pilot study using kernelled support tensor machine for distant failure prediction in lung SBRT. Medical Image Analysis. 50. 106–116. 26 indexed citations
18.
Qi, Hongliang, et al.. (2016). Iterative Image Reconstruction for Limited-Angle CT Using Optimized Initial Image. Computational and Mathematical Methods in Medicine. 2016. 1–9. 6 indexed citations
19.
Leng, Jing, Feng Wang, Jiaxiang Zhang, et al.. (2014). [Levels of complement components C3a and C5a in renal injury among trichloroethylene-sensitized BALB/c mice].. PubMed. 32(5). 335–9. 3 indexed citations
20.
Li, Shulong, Chong Li, Yeong‐Cheng Liou, & Jen-Chih Yao. (2009). Existence of solutions for variational inequalities on Riemannian manifolds. Nonlinear Analysis. 71(11). 5695–5706. 76 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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